Study Objectives: Acoustic analysis of isolated events and snoring by previous researchers suggests a correlation between individual acoustic features and individual site of collapse events. In this study, we hypothesized that multiparameter evaluation of snore sounds during natural sleep would provide a robust prediction of the predominant site of airway collapse. Methods: The audio signals of 58 obstructive sleep apnea patients were recorded simultaneously with full-night polysomnography. The site of collapse was determined by manual analysis of the shape of the airflow signal during hypopnea events and corresponding audio signal segments containing snore were manually extracted and processed. Machine learning algorithms were developed to automatically annotate the site of collapse of each hypopnea event into three classes (lateral wall, palate, and tongue base). The predominant site of collapse for a sleep period was determined from the individual hypopnea annotations and compared to the manually determined annotations. This was a retrospective study that used cross-validation to estimate performance. Results: Cluster analysis showed that the data fit well in two clusters with a mean silhouette coefficient of 0.79 and an accuracy of 68% for classifying tongue/non-tongue collapse. A classification model using linear discriminants achieved an overall accuracy of 81% for discriminating tongue/non-tongue predominant site of collapse and accuracy of 64% for all site of collapse classes. Conclusions: Our results reveal that the snore signal during hypopnea can provide information regarding the predominant site of collapse in the upper airway. Therefore, the audio signal recorded during sleep could potentially be used as a new tool in identifying the predominant site of collapse and consequently improving the treatment selection and outcome.

Objective : This study provides a novel approach for an automated system using a machine learning algorithm to predict the predominant site of upper airway collapse into four classes (‘lateral wall’, ‘palate’, ‘tongue-base’ related collapse or ‘multi-level’ site-of-collapse) in obstructive sleep apnoea (OSA) patients from the audio signal recorded during normal sleep. Approach : Snore sounds from 58 patients were recorded simultaneously with full-night polysomnography during sleep with a ceiling mounted microphone. The probable site-of-airway collapse was determined by manual analysis of the shape of the airflow signal during hypopnoea. Time and frequency features of the audio signal were extracted from each hypopnoea event to classify the audio signal into ‘lateral wall’, ‘palate’ and ‘tongue-base’ related collapse according to prior research. The data was divided into two sets. The Learning Set contained the data of the first 45 patients and was used for building the model. The Hidden Set contained the data from the remaining 13 patients and was used for testing the performance of the model. Feature selection was employed to boost the classification performance. The classification was carried out with a multi-class linear discriminant analysis classifier to classify the predominant site-of-collapse for a patient into the four classes. Performance was evaluated by comparing the automatic and manually labelled data based on the predominant site-of-collapse and calculating the accuracy. Main results : The model achieved an overall accuracy on the Hidden Set of 77% for discriminating tongue/non-tongue collapse and an accuracy of 62% accuracy for all site-of-collapse classes. Significance : Our results demonstrate that the audio signal recorded during sleep can successfully identify the site-of-collapse in the upper airway. The additional information regarding the obstruction site may assist clinicians in deciding the most appropriate treatment for OSA.

This paper presents a low complex design of a non-uniformly spaced digital finite impulse response (FIR) filter bank for digital hearing aid application. Frequency response masking (FRM) technique is used for the implementation of 10 non-uniformly spaced subband filters. FRM technique ensure the drastic reduction in the number of multipliers and adders in linear phase FIR filter. It employs two half-band filters as prototype filters, which leads to significant savings in terms of arithmetic operations. Further reduction can be achieved by using a masking filter with minimum order. The simulation result shows that the proposed filter bank with stop band attenuation of 80 dB can be achieved with 10 multipliers. The audiogram fitting with the selected audiogram gives a maximum matching error of 2 dB. From the audiogram matching result, it can be seen that the introduction of non-uniformly spaced filter bank improves the audiogram matching especially for sharp transition in hearing losses at low and high frequencies.

This paper provides the results of an unsupervised learning algorithm that characterize upper airway collapse in obstructive sleep apnoea (OSA) patients using snore signal during hypopnoea events. Knowledge regarding the site-of-collapse could improve the ability in choosing the most appropriate treatment for OSA and thereby improving the treatment outcome. In this study, we implemented an unsupervised k-means clustering algorithm to label the snore data during hypopnoea events. Audio data during sleep were recorded simultaneously with full-night polysomnography with a ceiling microphone. Various time and frequency features of audio signal during hypopnoea were extracted. A systematic evaluation method was implemented to find the optimal feature set and the optimal number of clusters using silhouette coefficients. Using these optimal feature sets, we clustered the snore data into two. Performance of the proposed model showed that the data fit well in two clusters with a mean silhouette coefficients of 0.79. Also, the clusters achieved an overall accuracy of 62% for predicting tongue/non-tongue related collapse.

A lot of effort has been devoted to the design of uniform and non-uniform filter banks for hearing aid applications. But in most of the cases, it never deals with large variation of hearing losses at mid frequencies. This paper presents a low complex design of a non-uniformly spaced digital finite impulse response (FIR) filter bank for digital hearing aid application. Frequency response masking (FRM) technique is used for the implementation of 8 non-uniformly spaced subband filters, with a single half-band filter as a prototype filter. With FRM technique and half-band filter, a drastic reduction in the number of multipliers and adders in linear phase FIR filter can be achieved. Further complexity-effective design can be achieved by producing masking filter from the prototype filter. FRM technique is achieved by cascading different combinations of prototype filter and its interpolated filters to produce subbands. The simulation results shows that, the proposed filter bank gives 120 dB attenuation with 13 multipliers only. The proposed FRM based filter bank can be effectively used for the audiogram matching with large variation of hearing threshold in mid frequencies.

In this study we investigated if audio signals may carry information related to the site of obstruction of the upper airway. The information regarding the site of collapse could improve obstructive sleep apnoea (OSA) treatment by allowing more individualized or structure-specific therapy. In this preliminary study, we developed an algorithm for automatically determining the site of collapse in 13 OSA patients through snoring analysis. Audio was recorded with a ceiling mounted microphone, simultaneously with full-night polysomnography during sleep. The surrogate measure of the site of airway collapse was identified by manual analysis of the nasal pressure signal. We extracted various time and frequency features of audio signal to classify the signal into "lateral wall", "palate" and "tongue base" related collapse. The classification was carried out with a Gaussian mixture model classifier. Performance of the proposed model showed that it can achieve an overall accuracy of 78.9±0.96% with specificity and PPV of (89.3±0.81%, 78±1.5%) (73.2±1.3% , 83.2±1.8%) (61.3±2.5%, 71.8±1.3%) for lateral wall, palate and tongue base related collapse respectively. Our preliminary results suggest that the audio signal may be helpful in identifying the site of obstruction and therefore maybe a useful tool for deciding appropriate therapy.